The subject matter discussed in the background section should not be assumed to be prior art merely as a result of its mention in the background section. Similarly, a problem mentioned in the background section or associated with the subject matter of the background section should not be assumed to have been previously recognized in the prior art. The subject matter in the background section merely represents different approaches, which in and of themselves may also correspond to implementations of the claimed technology.
Orthogonal Frequency Division Multiplexing (OFDM) is a modulation technique that is used in many wireless and telecommunications standards. OFDM is a modulation technique in which a high rate data stream is divided into many low rate parallel data streams and each is modulated using a separate narrowband close-spaced subcarrier thereby making the data stream less sensitive to frequency selective fading. A multiple-input multiple-output (MIMO) communication system employs multiple transmitting antennas and multiple receiving antennas for data transmission. A MIMO channel formed by the transmitting antennas and receiving antennas may be decomposed into independent channels. Each independent channel may also be referred to as a spatial subchannel of the MIMO channel. The MIMO system provides improved performance over that of a single-input single-output (SISO) communication system if the additional dimensionalities created by the multiple transmit and receive antennas are utilized.
There has been increased demand for throughput, spectral efficiency, and improved link reliability in wireless communication systems. To meet this demand, orthogonal frequency division multiplexing (OFDM) is combined with multiple input multiple output (MIMO) signal processing and employed in many wireless standards including wireless LAN. In such techniques, OFDM is employed as a multicarrier modulation scheme wherein data is transmitted on many orthogonal subcarriers to combat the effects of multipath channel, and MIMO signal processing is used to increase the throughput of the radio link by increasing the number of transmit and receive antennas. In a MIMO-OFDM system, modulated signals corresponding to independent data streams are transmitted from the multiple transmitting antennas of a transmitter that are then received by the multiple receiving antennas at the receiver. The receiver upon receiving the multiple signals, utilizes various MIMO detection schemes such as maximum likelihood (ML), zero forcing (ZF) and minimum mean squared error (MMSE) to detect the data symbols from multiple signals corresponding to each subcarrier after OFDM demodulation. A forward error correction (FEC) code is employed to improve the bit error rate performance in a MIMO-OFDM system also known as a coded MIMO-OFDM system. In coded MIMO-OFDM systems, the MIMO detector may provide either hard bits (either 1's or −1's) or soft bits (having the same sign as hard bits, but with magnitude indicating the reliability of the decision) to the FEC decoder. The performance of the FEC decoder may be improved by the soft bits, whose soft information is in the form of log likelihood ratios (LLR's). Therefore, it is desirable to employ a soft demapper in the coded MIMO-OFDM system to provide the soft bits to the FEC decoder.
A number of methods have been proposed which claim to provide the above mentioned facilities. In one such method, a soft MIMO demapper based on ZF and MMSE equalizers is proposed but its complexity is exponential in number of transmitted streams thus making its practical implementation difficult. In another such method, a receiver includes an inner decoder structure having a soft output M-algorithm (SOMA) based multiple-in multiple-out (MIMO) joint demapper that uses a SOMA-based MIMO detection process to perform joint inner demapping over each tone. In yet another method, a MMSE MIMO detector by using QR decomposition (QRD) of the augmented channel matrix is presented including a bias removal technique to decode the data on each stream by permuting the augmented channel matrix and computing its QRD on each stream. However, such soft demapping techniques proposed by the existing methods are cumbersome and are computationally intensive. Hence, there exists a need for providing a method and a soft demapper implementing the method for determining Log-Likelihood ratios that is practically implementable and achieves performance close to the near optimal-ML receiver even in delay spread channels.